The present invention relates generally to price modeling and particularly to a method and system for creating a price-forecasting tool.
In today's financial markets, the use of financial instruments known as “derivatives” have exponentially grown and is now common-place. A derivative is an investment vehicle with a value that is based on the value of another security or underlying asset. That is, a derivative is essentially a financial instrument that is derived from the future movement of something that cannot be predicted with certainty. By the late 1990's the Office of the Comptroller of the Currency estimates that commercial banks in the United States alone, held over twenty trillion dollars worth of derivative-based assets. Common examples of derivatives include futures contracts, forward contracts, options, and swaps.
The relationship between the value of a derivative and the underlying asset is not linear and can be very complex. Economists have developed pricing models in order to valuate certain types of derivatives. At the core of various derivative-pricing models are assumptions about how the price of the underlying asset (like a stock) may change over time. These pricing models provide probability distributions that describe the possible states of prices at different points in the future. Prices are generally modeled as a stochastic process, in which the values change over time in an uncertain manner. A particular type of stochastic process is the Markov process, where only the present state of the process (e.g., the current stock price) is relevant for predicting the future. The past history of the process is irrelevant.
A particular type of Markov process typically used to model prices is geometric Brownian motion (GBM). GBM, which is the basis of the vast majority of derivative pricing models, makes two key assumptions:                1. Price changes over small time intervals are independent, and therefore longer-term forecasts can be generated by repeatedly simulating small incremental changes in prices.        2. The distribution of future predicted prices is log-normal (LN).        
While there are many variations on the GBM approach to modeling asset or commodity prices, they all are fundamentally constrained by the two assumptions listed above. Consequently, while these approaches to modeling price may fit well in efficient, exchange-traded markets, they do not fit well in markets for commodities that are not traded on exchanges. Such markets are typically dominated by a handful of big buyers and big suppliers who negotiate prices directly. These markets tend to move not in a random fashion, as the “small independent intervals” assumption would suggest, but rather in cycles lasting from 6 months to several years. Within each cycle, periodically negotiated contract prices continue on a rising or falling trend, without deviation, until the market suddenly “turns” and prices head in the other direction. Therefore, the assumption that price changes over different horizons can all be modeled using the same model of changes over small, independent intervals is not good.
A co-pending patent application entitled “A Method and System For Creating A Price Forecasting Tool,” naming the applicant as a co-inventor (HPP number 200206487-1) (referred to herein as the “Co-pending Patent Application”) provides a method and system for forecasting prices of commodities by building a statistical model of price forecasts for each desired forecast horizon. The method of the Co-pending Patent Application involves first receiving historical data related to a commodity, defining a long-run average price trend based on the received historical data and creating a price forecasting tool based on the long-run average price trend. The price forecasting tool is capable of taking into account a market momentum of the commodity in order to generate a plurality of scenario prices of the commodity for a plurality of forecast horizons.
Although the Co-pending Patent Application provides a way to forecast pricing using price trends and short-term price momentum, other factors are involved in the complex market milieu that should be considered in order to more finely tune the price-forecasting method and system to reduce error. Accordingly, what is needed is a method and system for forecasting future commodity pricing that includes important market variables besides pricing. The method and system should be simple, cost effective and capable of being easily adapted to existing technology. The present invention addresses these needs.